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» Learning Partially Observable Deterministic Action Models
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ICML
2008
IEEE
14 years 8 months ago
Exploration scavenging
We examine the problem of evaluating a policy in the contextual bandit setting using only observations collected during the execution of another policy. We show that policy evalua...
John Langford, Alexander L. Strehl, Jennifer Wortm...
NIPS
2003
13 years 9 months ago
All learning is Local: Multi-agent Learning in Global Reward Games
In large multiagent games, partial observability, coordination, and credit assignment persistently plague attempts to design good learning algorithms. We provide a simple and ef...
Yu-Han Chang, Tracey Ho, Leslie Pack Kaelbling
ICML
2004
IEEE
14 years 8 months ago
Learning low dimensional predictive representations
Predictive state representations (PSRs) have recently been proposed as an alternative to partially observable Markov decision processes (POMDPs) for representing the state of a dy...
Matthew Rosencrantz, Geoffrey J. Gordon, Sebastian...
ICRA
2010
IEEE
142views Robotics» more  ICRA 2010»
13 years 6 months ago
Learning and planning high-dimensional physical trajectories via structured Lagrangians
— We consider the problem of finding sufficiently simple models of high-dimensional physical systems that are consistent with observed trajectories, and using these models to s...
Paul Vernaza, Daniel D. Lee, Seung-Joon Yi
NAACL
2010
13 years 5 months ago
An Efficient Algorithm for Easy-First Non-Directional Dependency Parsing
We present a novel deterministic dependency parsing algorithm that attempts to create the easiest arcs in the dependency structure first in a non-directional manner. Traditional d...
Yoav Goldberg, Michael Elhadad